3frac (problem 3.3.3)

Percentage Accurate: 70.3% → 99.8%

Time: 10.5s

Alternatives: 6

Speedup: 2.1×

Specification

\[\left|x\right| > 1\]

Math

\[\begin{array}{l} \\ \left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+ (- (/ 1.0 (+ x 1.0)) (/ 2.0 x)) (/ 1.0 (- x 1.0))))

C

double code(double x) {
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((1.0d0 / (x + 1.0d0)) - (2.0d0 / x)) + (1.0d0 / (x - 1.0d0))
end function

Java

public static double code(double x) {
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
}

Python

def code(x):
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0))

Julia

function code(x)
	return Float64(Float64(Float64(1.0 / Float64(x + 1.0)) - Float64(2.0 / x)) + Float64(1.0 / Float64(x - 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
end

Wolfram

code[x_] := N[(N[(N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 / x), $MachinePrecision]), $MachinePrecision] + N[(1.0 / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Initial Program: 70.3% accurate, 1.0× speedup

Math

\[\begin{array}{l} \\ \left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \end{array} \]

FPCore

(FPCore (x)
 :precision binary64
 (+ (- (/ 1.0 (+ x 1.0)) (/ 2.0 x)) (/ 1.0 (- x 1.0))))

C

double code(double x) {
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((1.0d0 / (x + 1.0d0)) - (2.0d0 / x)) + (1.0d0 / (x - 1.0d0))
end function

Java

public static double code(double x) {
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
}

Python

def code(x):
	return ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0))

Julia

function code(x)
	return Float64(Float64(Float64(1.0 / Float64(x + 1.0)) - Float64(2.0 / x)) + Float64(1.0 / Float64(x - 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = ((1.0 / (x + 1.0)) - (2.0 / x)) + (1.0 / (x - 1.0));
end

Wolfram

code[x_] := N[(N[(N[(1.0 / N[(x + 1.0), $MachinePrecision]), $MachinePrecision] - N[(2.0 / x), $MachinePrecision]), $MachinePrecision] + N[(1.0 / N[(x - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Alternative 1: 99.8% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{2}{x}}{x \cdot x + -1} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (/ 2.0 x) (+ (* x x) -1.0)))

C

double code(double x) {
	return (2.0 / x) / ((x * x) + -1.0);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (2.0d0 / x) / ((x * x) + (-1.0d0))
end function

Java

public static double code(double x) {
	return (2.0 / x) / ((x * x) + -1.0);
}

Python

def code(x):
	return (2.0 / x) / ((x * x) + -1.0)

Julia

function code(x)
	return Float64(Float64(2.0 / x) / Float64(Float64(x * x) + -1.0))
end

MATLAB

function tmp = code(x)
	tmp = (2.0 / x) / ((x * x) + -1.0);
end

Wolfram

code[x_] := N[(N[(2.0 / x), $MachinePrecision] / N[(N[(x * x), $MachinePrecision] + -1.0), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.9%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Add Preprocessing
3. Step-by-step derivation

   1. frac-sub N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{1 \cdot x - \left(x + 1\right) \cdot 2}{\left(x + 1\right) \cdot x}\right), \mathsf{/.f64}\left(\color{blue}{1}, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   2. associate-/r* N/A

      \[\leadsto \mathsf{+.f64}\left(\left(\frac{\frac{1 \cdot x - \left(x + 1\right) \cdot 2}{x + 1}}{x}\right), \mathsf{/.f64}\left(\color{blue}{1}, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\left(\frac{1 \cdot x - \left(x + 1\right) \cdot 2}{x + 1}\right), x\right), \mathsf{/.f64}\left(\color{blue}{1}, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(\left(1 \cdot x - \left(x + 1\right) \cdot 2\right), \left(x + 1\right)\right), x\right), \mathsf{/.f64}\left(1, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   5. *-lft-identity N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(\left(x - \left(x + 1\right) \cdot 2\right), \left(x + 1\right)\right), x\right), \mathsf{/.f64}\left(1, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   6. --lowering--.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{\_.f64}\left(x, \left(\left(x + 1\right) \cdot 2\right)\right), \left(x + 1\right)\right), x\right), \mathsf{/.f64}\left(1, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   7. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{\_.f64}\left(x, \mathsf{*.f64}\left(\left(x + 1\right), 2\right)\right), \left(x + 1\right)\right), x\right), \mathsf{/.f64}\left(1, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   8. +-commutative N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{\_.f64}\left(x, \mathsf{*.f64}\left(\left(1 + x\right), 2\right)\right), \left(x + 1\right)\right), x\right), \mathsf{/.f64}\left(1, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   9. +-lowering-+.f64 N/A

      \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{\_.f64}\left(x, \mathsf{*.f64}\left(\mathsf{+.f64}\left(1, x\right), 2\right)\right), \left(x + 1\right)\right), x\right), \mathsf{/.f64}\left(1, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   10. +-commutative N/A

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{\_.f64}\left(x, \mathsf{*.f64}\left(\mathsf{+.f64}\left(1, x\right), 2\right)\right), \left(1 + x\right)\right), x\right), \mathsf{/.f64}\left(1, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

   11. +-lowering-+.f64 74.0%

       \[\leadsto \mathsf{+.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{\_.f64}\left(x, \mathsf{*.f64}\left(\mathsf{+.f64}\left(1, x\right), 2\right)\right), \mathsf{+.f64}\left(1, x\right)\right), x\right), \mathsf{/.f64}\left(1, \mathsf{\_.f64}\left(x, 1\right)\right)\right) \]

4. Applied egg-rr 74.0%

   \[\leadsto \color{blue}{\frac{\frac{x - \left(1 + x\right) \cdot 2}{1 + x}}{x}} + \frac{1}{x - 1} \]
5. Step-by-step derivation

   1. div-inv N/A

      \[\leadsto \frac{x - \left(1 + x\right) \cdot 2}{1 + x} \cdot \frac{1}{x} + \frac{\color{blue}{1}}{x - 1} \]

   2. associate-*l/ N/A

      \[\leadsto \frac{\left(x - \left(1 + x\right) \cdot 2\right) \cdot \frac{1}{x}}{1 + x} + \frac{\color{blue}{1}}{x - 1} \]

   3. frac-add N/A

      \[\leadsto \frac{\left(\left(x - \left(1 + x\right) \cdot 2\right) \cdot \frac{1}{x}\right) \cdot \left(x - 1\right) + \left(1 + x\right) \cdot 1}{\color{blue}{\left(1 + x\right) \cdot \left(x - 1\right)}} \]

   4. +-commutative N/A

      \[\leadsto \frac{\left(\left(x - \left(1 + x\right) \cdot 2\right) \cdot \frac{1}{x}\right) \cdot \left(x - 1\right) + \left(1 + x\right) \cdot 1}{\left(x + 1\right) \cdot \left(\color{blue}{x} - 1\right)} \]

   5. difference-of-sqr-1 N/A

      \[\leadsto \frac{\left(\left(x - \left(1 + x\right) \cdot 2\right) \cdot \frac{1}{x}\right) \cdot \left(x - 1\right) + \left(1 + x\right) \cdot 1}{x \cdot x - \color{blue}{1}} \]

   6. metadata-eval N/A

      \[\leadsto \frac{\left(\left(x - \left(1 + x\right) \cdot 2\right) \cdot \frac{1}{x}\right) \cdot \left(x - 1\right) + \left(1 + x\right) \cdot 1}{x \cdot x - 1 \cdot \color{blue}{1}} \]

   7. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\left(\left(x - \left(1 + x\right) \cdot 2\right) \cdot \frac{1}{x}\right) \cdot \left(x - 1\right) + \left(1 + x\right) \cdot 1\right), \color{blue}{\left(x \cdot x - 1 \cdot 1\right)}\right) \]

6. Applied egg-rr 72.1%

   \[\leadsto \color{blue}{\frac{\left(\left(x + -2 \cdot \left(x + 1\right)\right) \cdot \frac{1}{x}\right) \cdot \left(x + -1\right) + \left(x + 1\right)}{x \cdot x + -1}} \]

7. Taylor expanded in x around 0

   \[\leadsto \mathsf{/.f64}\left(\color{blue}{\left(\frac{2}{x}\right)}, \mathsf{+.f64}\left(\mathsf{*.f64}\left(x, x\right), -1\right)\right) \]
8. Step-by-step derivation

   1. /-lowering-/.f64 99.8%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \mathsf{+.f64}\left(\color{blue}{\mathsf{*.f64}\left(x, x\right)}, -1\right)\right) \]

9. Simplified 99.8%

   \[\leadsto \frac{\color{blue}{\frac{2}{x}}}{x \cdot x + -1} \]
10. Add Preprocessing

Alternative 2: 98.8% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{\frac{2}{x}}{x}}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (/ (/ 2.0 x) x) x))

C

double code(double x) {
	return ((2.0 / x) / x) / x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = ((2.0d0 / x) / x) / x
end function

Java

public static double code(double x) {
	return ((2.0 / x) / x) / x;
}

Python

def code(x):
	return ((2.0 / x) / x) / x

Julia

function code(x)
	return Float64(Float64(Float64(2.0 / x) / x) / x)
end

MATLAB

function tmp = code(x)
	tmp = ((2.0 / x) / x) / x;
end

Wolfram

code[x_] := N[(N[(N[(2.0 / x), $MachinePrecision] / x), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 73.9%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{2}{{x}^{3}}} \]
4. Step-by-step derivation

   1. cube-mult N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(x \cdot x\right)}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{x \cdot {x}^{\color{blue}{2}}} \]

   3. associate-/r* N/A

      \[\leadsto \frac{\frac{2}{x}}{\color{blue}{{x}^{2}}} \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x}\right), \color{blue}{\left({x}^{2}\right)}\right) \]

   5. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left({\color{blue}{x}}^{2}\right)\right) \]

   6. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left(x \cdot \color{blue}{x}\right)\right) \]

   7. *-lowering-*.f64 98.9%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

5. Simplified 98.9%

   \[\leadsto \color{blue}{\frac{\frac{2}{x}}{x \cdot x}} \]
6. Step-by-step derivation

   1. associate-/r* N/A

      \[\leadsto \frac{\frac{\frac{2}{x}}{x}}{\color{blue}{x}} \]

   2. associate-/r* N/A

      \[\leadsto \frac{\frac{2}{x \cdot x}}{x} \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x \cdot x}\right), \color{blue}{x}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \left(x \cdot x\right)\right), x\right) \]

   5. *-lowering-*.f64 98.9%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, x\right)\right), x\right) \]

7. Applied egg-rr 98.9%

   \[\leadsto \color{blue}{\frac{\frac{2}{x \cdot x}}{x}} \]
8. Step-by-step derivation

   1. associate-/r* N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{\frac{2}{x}}{x}\right), x\right) \]

   2. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(\left(\frac{2}{x}\right), x\right), x\right) \]

   3. /-lowering-/.f64 98.9%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), x\right), x\right) \]

9. Applied egg-rr 98.9%

   \[\leadsto \frac{\color{blue}{\frac{\frac{2}{x}}{x}}}{x} \]
10. Add Preprocessing

Alternative 3: 98.8% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{2}{x \cdot x}}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (/ 2.0 (* x x)) x))

C

double code(double x) {
	return (2.0 / (x * x)) / x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (2.0d0 / (x * x)) / x
end function

Java

public static double code(double x) {
	return (2.0 / (x * x)) / x;
}

Python

def code(x):
	return (2.0 / (x * x)) / x

Julia

function code(x)
	return Float64(Float64(2.0 / Float64(x * x)) / x)
end

MATLAB

function tmp = code(x)
	tmp = (2.0 / (x * x)) / x;
end

Wolfram

code[x_] := N[(N[(2.0 / N[(x * x), $MachinePrecision]), $MachinePrecision] / x), $MachinePrecision]

Derivation

1. Initial program 73.9%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{2}{{x}^{3}}} \]
4. Step-by-step derivation

   1. cube-mult N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(x \cdot x\right)}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{x \cdot {x}^{\color{blue}{2}}} \]

   3. associate-/r* N/A

      \[\leadsto \frac{\frac{2}{x}}{\color{blue}{{x}^{2}}} \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x}\right), \color{blue}{\left({x}^{2}\right)}\right) \]

   5. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left({\color{blue}{x}}^{2}\right)\right) \]

   6. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left(x \cdot \color{blue}{x}\right)\right) \]

   7. *-lowering-*.f64 98.9%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

5. Simplified 98.9%

   \[\leadsto \color{blue}{\frac{\frac{2}{x}}{x \cdot x}} \]
6. Step-by-step derivation

   1. associate-/r* N/A

      \[\leadsto \frac{\frac{\frac{2}{x}}{x}}{\color{blue}{x}} \]

   2. associate-/r* N/A

      \[\leadsto \frac{\frac{2}{x \cdot x}}{x} \]

   3. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x \cdot x}\right), \color{blue}{x}\right) \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \left(x \cdot x\right)\right), x\right) \]

   5. *-lowering-*.f64 98.9%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, x\right)\right), x\right) \]

7. Applied egg-rr 98.9%

   \[\leadsto \color{blue}{\frac{\frac{2}{x \cdot x}}{x}} \]
8. Add Preprocessing

Alternative 4: 98.8% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{\frac{2}{x}}{x \cdot x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ (/ 2.0 x) (* x x)))

C

double code(double x) {
	return (2.0 / x) / (x * x);
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (2.0d0 / x) / (x * x)
end function

Java

public static double code(double x) {
	return (2.0 / x) / (x * x);
}

Python

def code(x):
	return (2.0 / x) / (x * x)

Julia

function code(x)
	return Float64(Float64(2.0 / x) / Float64(x * x))
end

MATLAB

function tmp = code(x)
	tmp = (2.0 / x) / (x * x);
end

Wolfram

code[x_] := N[(N[(2.0 / x), $MachinePrecision] / N[(x * x), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.9%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{2}{{x}^{3}}} \]
4. Step-by-step derivation

   1. cube-mult N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(x \cdot x\right)}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{x \cdot {x}^{\color{blue}{2}}} \]

   3. associate-/r* N/A

      \[\leadsto \frac{\frac{2}{x}}{\color{blue}{{x}^{2}}} \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x}\right), \color{blue}{\left({x}^{2}\right)}\right) \]

   5. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left({\color{blue}{x}}^{2}\right)\right) \]

   6. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left(x \cdot \color{blue}{x}\right)\right) \]

   7. *-lowering-*.f64 98.9%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

5. Simplified 98.9%

   \[\leadsto \color{blue}{\frac{\frac{2}{x}}{x \cdot x}} \]
6. Add Preprocessing

Alternative 5: 98.1% accurate, 2.1× speedup

Math

\[\begin{array}{l} \\ \frac{2}{x \cdot \left(x \cdot x\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (* x (* x x))))

C

double code(double x) {
	return 2.0 / (x * (x * x));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0 / (x * (x * x))
end function

Java

public static double code(double x) {
	return 2.0 / (x * (x * x));
}

Python

def code(x):
	return 2.0 / (x * (x * x))

Julia

function code(x)
	return Float64(2.0 / Float64(x * Float64(x * x)))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (x * (x * x));
end

Wolfram

code[x_] := N[(2.0 / N[(x * N[(x * x), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Derivation

1. Initial program 73.9%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Add Preprocessing

3. Taylor expanded in x around inf

   \[\leadsto \color{blue}{\frac{2}{{x}^{3}}} \]
4. Step-by-step derivation

   1. cube-mult N/A

      \[\leadsto \frac{2}{x \cdot \color{blue}{\left(x \cdot x\right)}} \]

   2. unpow2 N/A

      \[\leadsto \frac{2}{x \cdot {x}^{\color{blue}{2}}} \]

   3. associate-/r* N/A

      \[\leadsto \frac{\frac{2}{x}}{\color{blue}{{x}^{2}}} \]

   4. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\left(\frac{2}{x}\right), \color{blue}{\left({x}^{2}\right)}\right) \]

   5. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left({\color{blue}{x}}^{2}\right)\right) \]

   6. unpow2 N/A

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \left(x \cdot \color{blue}{x}\right)\right) \]

   7. *-lowering-*.f64 98.9%

      \[\leadsto \mathsf{/.f64}\left(\mathsf{/.f64}\left(2, x\right), \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right) \]

5. Simplified 98.9%

   \[\leadsto \color{blue}{\frac{\frac{2}{x}}{x \cdot x}} \]
6. Step-by-step derivation

   1. associate-/r* N/A

      \[\leadsto \frac{2}{\color{blue}{x \cdot \left(x \cdot x\right)}} \]

   2. /-lowering-/.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \color{blue}{\left(x \cdot \left(x \cdot x\right)\right)}\right) \]

   3. *-lowering-*.f64 N/A

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \color{blue}{\left(x \cdot x\right)}\right)\right) \]

   4. *-lowering-*.f64 98.1%

      \[\leadsto \mathsf{/.f64}\left(2, \mathsf{*.f64}\left(x, \mathsf{*.f64}\left(x, \color{blue}{x}\right)\right)\right) \]

7. Applied egg-rr 98.1%

   \[\leadsto \color{blue}{\frac{2}{x \cdot \left(x \cdot x\right)}} \]
8. Add Preprocessing

Alternative 6: 5.1% accurate, 5.0× speedup

Math

\[\begin{array}{l} \\ \frac{-2}{x} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ -2.0 x))

C

double code(double x) {
	return -2.0 / x;
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = (-2.0d0) / x
end function

Java

public static double code(double x) {
	return -2.0 / x;
}

Python

def code(x):
	return -2.0 / x

Julia

function code(x)
	return Float64(-2.0 / x)
end

MATLAB

function tmp = code(x)
	tmp = -2.0 / x;
end

Wolfram

code[x_] := N[(-2.0 / x), $MachinePrecision]

Derivation

1. Initial program 73.9%

   \[\left(\frac{1}{x + 1} - \frac{2}{x}\right) + \frac{1}{x - 1} \]
2. Add Preprocessing

3. Taylor expanded in x around 0

   \[\leadsto \color{blue}{\frac{-2}{x}} \]
4. Step-by-step derivation

   1. /-lowering-/.f64 5.2%

      \[\leadsto \mathsf{/.f64}\left(-2, \color{blue}{x}\right) \]

5. Simplified 5.2%

   \[\leadsto \color{blue}{\frac{-2}{x}} \]
6. Add Preprocessing

Developer Target 1: 99.2% accurate, 1.7× speedup

Math

\[\begin{array}{l} \\ \frac{2}{x \cdot \left(x \cdot x - 1\right)} \end{array} \]

FPCore

(FPCore (x) :precision binary64 (/ 2.0 (* x (- (* x x) 1.0))))

C

double code(double x) {
	return 2.0 / (x * ((x * x) - 1.0));
}

Fortran

real(8) function code(x)
    real(8), intent (in) :: x
    code = 2.0d0 / (x * ((x * x) - 1.0d0))
end function

Java

public static double code(double x) {
	return 2.0 / (x * ((x * x) - 1.0));
}

Python

def code(x):
	return 2.0 / (x * ((x * x) - 1.0))

Julia

function code(x)
	return Float64(2.0 / Float64(x * Float64(Float64(x * x) - 1.0)))
end

MATLAB

function tmp = code(x)
	tmp = 2.0 / (x * ((x * x) - 1.0));
end

Wolfram

code[x_] := N[(2.0 / N[(x * N[(N[(x * x), $MachinePrecision] - 1.0), $MachinePrecision]), $MachinePrecision]), $MachinePrecision]

Reproduce

herbie shell --seed 2024192 
(FPCore (x)
  :name "3frac (problem 3.3.3)"
  :precision binary64
  :pre (> (fabs x) 1.0)

  :alt
  (! :herbie-platform default (/ 2 (* x (- (* x x) 1))))

  (+ (- (/ 1.0 (+ x 1.0)) (/ 2.0 x)) (/ 1.0 (- x 1.0))))